Genetic algorithm with hierarchical evaluation strategy (HESGA) [1]} utilises the elite archive to preserve excellent individuals, from which a parent can be selected to generate offspring. Meanwhile, the fast evaluation strategy has been introduced in HESGA to speed up optimisation and save computational resources. Furthermore, HSEGA uses the single point mutation as in [2]}, [3]}, [4]} which is one factor for preventing GA from getting stuck in local optima and maintains the diversity of population [5]}. However, we believe that the mutation operation in HSEGA can be further explored as only classical mutation operation was used in HSEGA. Therefore, in this research, we propose a tree-structured mutation (TSM) which employs the tree structure to store hierarchical historical information to guide mutation. In this way, GA can explore the whole search space more adaptively and effectively. Meanwhile, the idea of TSM is also compatible and scalable to other HPO algorithms that hierarchise historical information for future discovery.
